import os
import json
from collections import defaultdict
import matplotlib.pyplot as plt
import pandas as pd

# === CONFIGURATION ===
IPL_JSON_PATH = "../ipl_json"  # path to folder containing IPL JSON files
SEASONS_FILTER = set({"2024"})       # example: {"2020", "2021"}; empty set = include all
SELECTED_PLAYERS = set()     # example: {"V Kohli", "MS Dhoni"}; empty set = include all
MIN_BALLS_FACED = 100          # minimum balls faced to include player in output

# === DATA STRUCTURE ===
player_stats = defaultdict(lambda: {
    'runs': 0,
    'outs': 0,
    'balls': 0,
    'baseline_runs': 0,
    'baseline_outs': 0,
    'baseline_balls': 0,
})

def extract_top6(info):
    players = info.get("players", {})
    top6 = set()
    for team in players.values():
        top6.update(team[:6])
    return top6

def process_match(filepath):
    with open(filepath, "r", encoding="utf-8") as f:
        match = json.load(f)

    season = str(match["info"].get("season"))
    if SEASONS_FILTER and season not in SEASONS_FILTER:
        return

    top6 = extract_top6(match["info"])
    match_player_data = defaultdict(lambda: {'runs': 0, 'balls': 0, 'outs': 0})

    for inning in match.get("innings", []):
        for over in inning.get("overs", []):
            for delivery in over.get("deliveries", []):
                batter = delivery.get("batter")
                runs = delivery.get("runs", {}).get("batter", 0)

                match_player_data[batter]['runs'] += runs
                match_player_data[batter]['balls'] += 1

                if "wickets" in delivery:
                    for w in delivery["wickets"]:
                        if w.get("kind") != "run out":  # Ignore run-outs if you want
                            out_player = w.get("player_out")
                            match_player_data[out_player]['outs'] += 1

    # Aggregate per match
    baseline_runs = sum(match_player_data[p]['runs'] for p in top6)
    baseline_outs = sum(match_player_data[p]['outs'] for p in top6)
    baseline_balls = sum(match_player_data[p]['balls'] for p in top6)

    for player, data in match_player_data.items():
        if SELECTED_PLAYERS and player not in SELECTED_PLAYERS:
            continue

        player_stats[player]['runs'] += data['runs']
        player_stats[player]['balls'] += data['balls']
        player_stats[player]['outs'] += data['outs']
        player_stats[player]['baseline_runs'] += baseline_runs
        player_stats[player]['baseline_outs'] += baseline_outs
        player_stats[player]['baseline_balls'] += baseline_balls

# === MAIN PROCESSING ===
all_files = [os.path.join(IPL_JSON_PATH, f) for f in os.listdir(IPL_JSON_PATH) if f.endswith(".json")]

for f in all_files:
    process_match(f)

# === COMPUTE TRUE AVERAGE & TRUE STRIKE RATE ===
results = []

for player, data in player_stats.items():
    if data['outs'] == 0 or data['baseline_outs'] == 0 or data['balls'] < MIN_BALLS_FACED or data['baseline_balls'] == 0:
        continue  # skip if average or SR is undefined or not enough data

    avg = data['runs'] / data['outs']
    baseline_avg = data['baseline_runs'] / data['baseline_outs']
    true_avg_pct = (avg / baseline_avg - 1) * 100

    strike_rate = data['runs'] / data['balls'] * 100
    baseline_sr = data['baseline_runs'] / data['baseline_balls'] * 100
    true_sr_pct = (strike_rate / baseline_sr - 1) * 100

    results.append({
        'Player': player,
        'Average': round(avg, 2),
        'Baseline Avg': round(baseline_avg, 2),
        'True Average (%)': round(true_avg_pct, 2),
        'Strike Rate': round(strike_rate, 2),
        'Baseline SR': round(baseline_sr, 2),
        'True SR (%)': round(true_sr_pct, 2),
        'Runs': data['runs'],
        'Outs': data['outs'],
        'Balls': data['balls'],
    })

# === OUTPUT ===
df = pd.DataFrame(results)
df.sort_values("True Average (%)", ascending=False, inplace=True)
display(df)  # for Jupyter notebooks

# === COMBINED TRUE AVG vs TRUE SR PLOT ===
plt.figure(figsize=(8,6))
plt.scatter(df['True SR (%)'], df['True Average (%)'])
for _, row in df.iterrows():
    plt.annotate(row['Player'], (row['True SR (%)'], row['True Average (%)']), fontsize=8)
plt.axhline(0, color='gray', linestyle='--')
plt.axvline(0, color='gray', linestyle='--')
plt.xlabel("True Strike Rate (%)")
plt.ylabel("True Average (%)")
plt.title("True Average vs True Strike Rate: IPL Batters")
plt.grid(True)
plt.show()

df.to_csv('ipl_player_stats.csv', index=False)

# Normalize True Average and True SR using min-max scaling
from sklearn.preprocessing import MinMaxScaler

scaler = MinMaxScaler()
df[['Norm True Avg', 'Norm True SR']] = scaler.fit_transform(df[['True Average (%)', 'True SR (%)']])

# Equal weight for both components
df['Total Batting Impact'] = (df['Norm True Avg'] + df['Norm True SR']) / 2
df.sort_values('Total Batting Impact', ascending=False, inplace=True)
display(df[['Player', 'True Average (%)', 'True SR (%)', 'Total Batting Impact']])

# Optional: bar chart of top 15 batters by impact
top_n = 15
plt.figure(figsize=(10,6))
top_df = df.head(top_n)
plt.barh(top_df['Player'], top_df['Total Batting Impact'], color='skyblue')
plt.xlabel('Total Batting Impact Score')
for index, value in enumerate(top_df['Total Batting Impact']):
    plt.text(value, index, f'{value:.2f}', va='center')
plt.title(f'Top {top_n} IPL Batters by Combined Impact')
plt.gca().invert_yaxis()

plt.show()

# === TRUE WICKETS FOR BOWLERS ===
from collections import defaultdict

bowler_wickets = defaultdict(float)

def get_over_phase(over_num):
    if over_num < 6:
        return 2.0  # Powerplay
    elif over_num < 15:
        return 1.0  # Middle
    else:
        return 0.5  # Death

for f in all_files:
    with open(f, "r", encoding="utf-8") as match_file:
        match = json.load(match_file)

    for innings in match.get("innings", []):
        for over in innings.get("overs", []):
            over_num = over.get("over", 0)
            for delivery in over.get("deliveries", []):
                bowler = delivery.get("bowler")
                if "wickets" in delivery:
                    for w in delivery["wickets"]:
                        kind = w.get("kind")
                        if kind and kind != "run out":
                            phase_weight = get_over_phase(over_num)
                            bowler_wickets[bowler] += phase_weight

# Create a DataFrame of true wickets
bowler_df = pd.DataFrame([
    {"Bowler": b, "True Wickets": round(w, 2)} for b, w in bowler_wickets.items()
]).sort_values("True Wickets", ascending=False)

display(bowler_df.head(20))

# === TRUE ECONOMY RATE BY PHASE ===
from collections import defaultdict

# Store bowler stats per phase across matches
bowler_phase_stats = defaultdict(lambda: {
    "powerplay_runs": 0, "powerplay_balls": 0,
    "middle_runs": 0, "middle_balls": 0,
    "death_runs": 0, "death_balls": 0,
    "true_economy_contributions": []
})

def get_phase(over_num):
    if over_num < 6:
        return "powerplay"
    elif over_num < 15:
        return "middle"
    else:
        return "death"

for f in all_files:
    with open(f, "r", encoding="utf-8") as match_file:
        match = json.load(match_file)

    # Temp structure to hold all bowlers’ phase stats for this match
    match_phase_runs = {
        "powerplay": defaultdict(lambda: {"runs": 0, "balls": 0}),
        "middle": defaultdict(lambda: {"runs": 0, "balls": 0}),
        "death": defaultdict(lambda: {"runs": 0, "balls": 0}),
    }

    for innings in match.get("innings", []):
        for over in innings.get("overs", []):
            over_num = over.get("over", 0)
            phase = get_phase(over_num)
            for delivery in over.get("deliveries", []):
                bowler = delivery.get("bowler")
                runs = delivery.get("runs", {}).get("total", 0)

                match_phase_runs[phase][bowler]["runs"] += runs
                match_phase_runs[phase][bowler]["balls"] += 1

    # After processing match, compute baseline and true economy for each bowler per phase
    for phase in ["powerplay", "middle", "death"]:
        all_runs = sum(b["runs"] for b in match_phase_runs[phase].values())
        all_balls = sum(b["balls"] for b in match_phase_runs[phase].values())
        if all_balls == 0:
            continue

        baseline_er = all_runs / (all_balls / 6)  # runs per over

        for bowler, stats in match_phase_runs[phase].items():
            if stats["balls"] == 0:
                continue
            er = stats["runs"] / (stats["balls"] / 6)
            if baseline_er == 0:
                continue  # skip this comparison to avoid division by zero
            true_er_pct = ((er / baseline_er) - 1) * 100

            # Update global stats
            bowler_phase_stats[bowler][f"{phase}_runs"] += stats["runs"]
            bowler_phase_stats[bowler][f"{phase}_balls"] += stats["balls"]
            bowler_phase_stats[bowler]["true_economy_contributions"].append(true_er_pct)

# Final output: average true economy contribution per bowler
bowler_true_economy = []
for bowler, data in bowler_phase_stats.items():
    all_contributions = data["true_economy_contributions"]
    if not all_contributions:
        continue
    avg_true_economy = sum(all_contributions) / len(all_contributions)
    bowler_true_economy.append({
        "Bowler": bowler,
        "True Economy (%)": round(avg_true_economy, 2),
        "Matches": len(all_contributions)
    })

bowler_er_df = pd.DataFrame(bowler_true_economy).sort_values("True Economy (%)")
display(bowler_er_df.head(20))

# === TRUE RUNS CONCEDED (refined) ===

true_runs_conceded = []

for bowler, data in bowler_phase_stats.items():
    total_balls = (
        data['powerplay_balls'] +
        data['middle_balls'] +
        data['death_balls']
    )
    if total_balls == 0:
        continue

    total_runs = (
        data['powerplay_runs'] +
        data['middle_runs'] +
        data['death_runs']
    )

    overs = total_balls / 6
    raw_er = total_runs / overs if overs else 0

    contributions = data['true_economy_contributions']
    avg_true_er_pct = sum(contributions) / len(contributions) if contributions else 0

    # Apply true economy adjustment
    true_er = raw_er * (1 + avg_true_er_pct / 100)
    true_runs = true_er * overs  # final adjusted runs conceded

    true_runs_conceded.append({
        "Bowler": bowler,
        "Balls": total_balls,
        "Raw ER": round(raw_er, 2),
        "True ER (%)": round(avg_true_er_pct, 2),
        "True ER": round(true_er, 2),
        "True Runs Conceded": round(true_runs, 2)
    })

true_runs_df = pd.DataFrame(true_runs_conceded).sort_values("True Runs Conceded", ascending=False)
display(true_runs_df.tail(20))

# === TRUE BOWLING AVERAGE ===
# Set minimum threshold for career deliveries bowled
MIN_BALLS_BOWLED = 300

# Get total balls per bowler from phase stats
bowler_total_balls = {
    b: data['powerplay_balls'] + data['middle_balls'] + data['death_balls']
    for b, data in bowler_phase_stats.items()
}

# Filter true_runs_df and bowler_df to only include bowlers with enough balls
qualified_bowlers = {b for b, balls in bowler_total_balls.items() if balls >= MIN_BALLS_BOWLED}
true_runs_df = true_runs_df[true_runs_df['Bowler'].isin(qualified_bowlers)]
bowler_df = bowler_df[bowler_df['Bowler'].isin(qualified_bowlers)]


# Merge true runs and true wickets
true_avg_df = pd.merge(true_runs_df, bowler_df, on="Bowler", how="inner")

# Filter out bowlers with 0 true wickets to avoid division by zero
true_avg_df = true_avg_df[true_avg_df["True Wickets"] > 0].copy()

# Calculate true average
true_avg_df["True Bowling Average"] = true_avg_df["True Runs Conceded"] / true_avg_df["True Wickets"]
true_avg_df["True Bowling Average"] = true_avg_df["True Bowling Average"].round(2)

# Sort by most efficient average (lowest)
true_avg_df.sort_values("True Bowling Average", inplace=True)
display(true_avg_df[["Bowler", "True Runs Conceded", "True Wickets", "True Bowling Average"]].head(20))

# Filter top 50 by lowest True Average and top 50 by lowest True ER
top_avg_bowlers = set(true_avg_df.nsmallest(50, 'True Bowling Average')["Bowler"])
top_er_bowlers = set(true_avg_df.nsmallest(50, 'True ER')["Bowler"])

# Intersection of both sets
top_bowlers = top_avg_bowlers & top_er_bowlers

# Filter the DataFrame
top_df = true_avg_df[true_avg_df["Bowler"].isin(top_bowlers)].copy()

plt.figure(figsize=(8,6))
plt.scatter(top_df['True ER'], top_df['True Bowling Average'])

for _, row in top_df.iterrows():
    plt.annotate(row['Bowler'], (row['True ER'], row['True Bowling Average']), fontsize=8)

plt.xlabel("True Economy Rate (runs per over)")
plt.ylabel("True Bowling Average (runs per wicket)")
plt.title("Top 50 IPL Bowlers: True Avg vs True Economy")
plt.axhline(top_df['True Bowling Average'].mean(), color='gray', linestyle='--', label='Avg True Avg')
plt.axvline(top_df['True ER'].mean(), color='gray', linestyle='--', label='Avg True ER')
plt.grid(True)
plt.legend()
plt.show()

from sklearn.preprocessing import MinMaxScaler

# We inverse the metrics so that lower values give higher normalized impact
top_df = top_df.copy()
scaler = MinMaxScaler()

top_df[['Norm Avg', 'Norm ER']] = scaler.fit_transform(
    top_df[['True Bowling Average', 'True ER']].apply(lambda x: -x)
)

# Combine equally for Total Bowling Impact
top_df['Total Bowling Impact'] = (top_df['Norm Avg'] + top_df['Norm ER']) / 2
top_df.sort_values('Total Bowling Impact', ascending=False, inplace=True)

display(top_df[['Bowler', 'True Bowling Average', 'True ER', 'Total Bowling Impact']].head(20))

# === BAR CHART: TOP 20 TOTAL BOWLING IMPACT ===

top_n = 20
impact_df = top_df.head(top_n).sort_values("Total Bowling Impact", ascending=True)  # for horizontal bar

plt.figure(figsize=(10, 6))
plt.barh(impact_df['Bowler'], impact_df['Total Bowling Impact'], color='seagreen')
plt.xlabel("Total Bowling Impact Score")
plt.title(f"Top {top_n} IPL Bowlers by Total Bowling Impact")
plt.grid(axis='x')
plt.tight_layout()
plt.show()

# === TABLE: BOWLING IMPACT POINTS ===

top_df['Bowling Impact Points'] = top_df['Total Bowling Impact'] * top_df['Balls']
top_df.sort_values('Bowling Impact Points', ascending=False, inplace=True)

display(top_df[['Bowler', 'Balls', 'Total Bowling Impact', 'Bowling Impact Points']].head(20))

# === BAR CHART: TOP 20 BOWLING IMPACT POINTS ===

top_n = 20
impact_points_df = top_df.head(top_n).sort_values("Bowling Impact Points", ascending=True)

plt.figure(figsize=(10, 6))
plt.barh(impact_points_df['Bowler'], impact_points_df['Bowling Impact Points'], color='darkorange')
plt.xlabel("Bowling Impact Points")
plt.title(f"Top {top_n} IPL Bowlers by Bowling Impact Points")
plt.grid(axis='x')
plt.tight_layout()
plt.show()

import plotly.io as pio
pio.renderers.default = 'notebook'
import plotly.express as px

scatter_df = top_df.nlargest(50, 'Total Bowling Impact').copy()

fig = px.scatter(
    scatter_df,
    x='True Runs Conceded',
    y='Total Bowling Impact',
    size='Balls',
    text='Bowler',
    hover_data={
        'True Runs Conceded': ':.1f',
        'Total Bowling Impact': ':.3f',
        'Balls': True,
        'Bowler': False
    },
    color='Total Bowling Impact',
    color_continuous_scale='Viridis',
    title='Top 50 IPL Bowlers: True Runs vs Total Bowling Impact',
    height=600
)
fig.update_traces(textposition='top center')
fig.update_layout(xaxis_title='True Runs Conceded', yaxis_title='Total Bowling Impact')
fig.show()

	Player	Average	Baseline Avg	True Average (%)	Strike Rate	Baseline SR	True SR (%)	Runs	Outs	Balls
22	Shashank Singh	59.00	27.75	112.61	157.33	139.70	12.62	354	6	225
14	T Stubbs	63.00	31.99	96.93	185.29	157.49	17.66	378	6	204
44	N Pooran	62.38	34.21	82.34	168.58	142.59	18.23	499	8	296
0	V Kohli	61.75	35.53	73.81	149.09	154.09	-3.24	741	12	497
49	B Sai Sudharsan	52.70	31.19	68.94	135.82	140.06	-3.02	527	10	388
...	...	...	...	...	...	...	...	...	...	...
27	RK Singh	18.67	31.33	-40.42	143.59	151.58	-5.27	168	9	117
56	HH Pandya	18.00	31.89	-43.56	140.26	147.20	-4.72	216	12	154
7	AM Rahane	20.17	35.84	-43.73	121.00	141.44	-14.45	242	12	200
43	DJ Hooda	18.12	33.42	-45.77	135.51	142.12	-4.65	145	8	107
47	WP Saha	15.11	29.30	-48.43	113.33	139.11	-18.53	136	9	120

	Player	True Average (%)	True SR (%)	Total Batting Impact
22	Shashank Singh	112.61	12.62	0.803878
14	T Stubbs	96.93	17.66	0.796432
44	N Pooran	82.34	18.23	0.755796
64	J Fraser-McGurk	32.86	36.59	0.752391
25	VR Iyer	67.36	3.98	0.592693
...	...	...	...	...
40	Q de Kock	-37.91	-10.82	0.144756
11	DA Warner	-29.07	-15.28	0.135711
7	AM Rahane	-43.73	-14.45	0.096985
31	AK Markram	-24.36	-24.52	0.074733
47	WP Saha	-48.43	-18.53	0.049010

	Bowler	True Wickets
94	R Ashwin	214.0
7	B Kumar	203.0
16	PP Chawla	193.5
2	YS Chahal	192.5
46	UT Yadav	176.0
39	SP Narine	171.5
19	Sandeep Sharma	170.0
44	Harbhajan Singh	167.0
18	TA Boult	160.5
36	JJ Bumrah	159.5
43	A Mishra	159.0
37	SL Malinga	153.0
50	RA Jadeja	151.5
4	Rashid Khan	144.5
82	Mohammed Shami	144.0
120	I Sharma	139.0
29	Z Khan	137.5
55	MM Sharma	136.5
103	DJ Bravo	135.5
42	DL Chahar	131.0

	Bowler	True Economy (%)	Matches
518	AC Gilchrist	-100.00	1
535	Sachin Baby	-60.48	2
292	R Ravindra	-44.87	2
93	NB Singh	-41.20	2
306	N Wadhera	-32.74	1
237	SS Iyer	-29.26	1
409	SM Harwood	-23.48	8
291	MP Yadav	-22.60	7
243	Ramandeep Singh	-21.60	5
229	Shashank Singh	-21.51	3
183	DA Warner	-20.71	1
277	JE Root	-19.41	1
311	DS Rathi	-19.04	6
233	A Badoni	-18.06	5
420	AM Rahane	-17.41	1
28	SP Narine	-15.59	431
330	GD McGrath	-15.36	28
210	RV Patel	-15.33	2
245	M Pathirana	-15.31	40
21	JJ Bumrah	-15.09	354

	Bowler	Balls	Raw ER	True ER (%)	True ER	True Runs Conceded
297	M Shahrukh Khan	12	8.00	-13.09	6.95	13.91
277	JE Root	12	7.50	-19.41	6.04	12.09
225	D Brevis	3	16.00	41.33	22.61	11.31
307	Atharva Taide	1	24.00	172.73	65.45	10.91
157	SN Khan	2	18.00	78.79	32.18	10.73
93	NB Singh	25	4.32	-41.20	2.54	10.58
505	SPD Smith	2	15.00	103.12	30.47	10.16
520	SA Yadav	6	8.00	20.16	9.61	9.61
313	Ishan Kishan	1	24.00	131.25	55.50	9.25
434	RS Gavaskar	6	8.00	13.13	9.05	9.05
306	N Wadhera	12	6.50	-32.74	4.37	8.74
482	Y Gnaneswara Rao	6	7.00	14.90	8.04	8.04
416	LA Carseldine	7	5.14	7.14	5.51	6.43
484	SS Mundhe	7	5.14	-3.90	4.94	5.77
237	SS Iyer	6	7.00	-29.26	4.95	4.95
420	AM Rahane	6	5.00	-17.41	4.13	4.13
292	R Ravindra	12	3.50	-44.87	1.93	3.86
535	Sachin Baby	10	4.80	-60.48	1.90	3.16
183	DA Warner	2	6.00	-20.71	4.76	1.59
518	AC Gilchrist	1	0.00	-100.00	0.00	0.00

Setup + True SR and True Bat Avg.¶

Batting Impact¶

Bowling Impact¶

True Wickets¶

True Economy¶

True Runs Conceded¶

Bowling impact¶

Bowling impact score¶

	Bowler	True Runs Conceded	True Wickets	True Bowling Average
195	AD Mascarenhas	388.11	30.0	12.94
194	L Ngidi	399.12	25.5	15.65
183	MF Maharoof	496.91	30.0	16.56
196	GD McGrath	309.77	18.5	16.74
179	BW Hilfenhaus	526.93	30.5	17.28
42	DL Chahar	2307.11	131.0	17.61
148	DE Bollinger	684.42	37.5	18.25
131	JR Hazlewood	822.16	44.0	18.69
104	S Aravind	1081.56	57.5	18.81
95	MA Starc	1157.81	61.5	18.83
186	KW Richardson	491.79	25.5	19.29
66	MM Patel	1796.37	91.5	19.63
26	Z Khan	2781.99	137.5	20.23
22	SL Malinga	3120.49	153.0	20.40
140	DP Nannes	745.31	36.5	20.42
19	JJ Bumrah	3260.65	159.5	20.44
153	Mohsin Khan	673.36	32.5	20.72
17	TA Boult	3349.53	160.5	20.87
86	S Sreesanth	1356.29	65.0	20.87
155	Harshit Rana	653.36	31.0	21.08

	Bowler	True Bowling Average	True ER	Total Bowling Impact
196	GD McGrath	16.74	5.65	0.821092
183	MF Maharoof	16.56	6.76	0.550672
194	L Ngidi	15.65	7.00	0.533215
195	AD Mascarenhas	12.94	7.51	0.532663
19	JJ Bumrah	20.44	6.14	0.523777
9	SP Narine	23.13	5.71	0.505169
43	DW Steyn	21.56	6.01	0.503710
120	A Kumble	23.51	5.67	0.497329
22	SL Malinga	20.40	6.30	0.485459
23	Rashid Khan	21.17	6.28	0.454232
148	DE Bollinger	18.25	6.84	0.451005
140	DP Nannes	20.42	6.49	0.436779
2	R Ashwin	22.77	6.22	0.393978
180	M Pathirana	23.22	6.23	0.370279
96	JC Archer	21.24	6.72	0.340384
56	CV Varun	21.13	7.05	0.262648
26	Z Khan	20.23	7.33	0.234669
131	JR Hazlewood	18.69	7.64	0.229284
99	Iqbal Abdulla	22.04	7.15	0.194679
4	B Kumar	23.26	6.98	0.179953

	Bowler	Balls	Total Bowling Impact	Bowling Impact Points
9	SP Narine	4170	0.505169	2106.556582
2	R Ashwin	4703	0.393978	1852.878032
19	JJ Bumrah	3185	0.523777	1668.229992
22	SL Malinga	2974	0.485459	1443.755993
23	Rashid Khan	2925	0.454232	1328.628843
43	DW Steyn	2282	0.503710	1149.465529
4	B Kumar	4060	0.179953	730.610964
26	Z Khan	2276	0.234669	534.106890
120	A Kumble	983	0.497329	488.874332
56	CV Varun	1672	0.262648	439.147432
96	JC Archer	1033	0.340384	351.616311
140	DP Nannes	689	0.436779	300.940730
148	DE Bollinger	600	0.451005	270.603015
196	GD McGrath	329	0.821092	270.139360
183	MF Maharoof	441	0.550672	242.846521
180	M Pathirana	503	0.370279	186.250282
99	Iqbal Abdulla	943	0.194679	183.581982
194	L Ngidi	342	0.533215	182.359382
195	AD Mascarenhas	310	0.532663	165.125628
5	YS Chahal	3647	0.045226	164.939698